Classical stepwise regression techniques have found favor in applications where the numbers of inputs, independent variables, or exogenous variables are extremely large and their measurable effect on the target and other inputs are unmanageable to measure. Classical statistics teaches that the targets should be plotted versus the inputs, the inputs versus other inputs and the correlation statistics. Some theorists suggest that one should have a solid understanding of the dynamics of the process and then force this known structure through a functionally efficient modeling environment.
However such techniques can be cumbersome and resource intensive to use, such as, for example, in studying Direct to Consumer (DTC) advertising in the pharmaceutical industry. Pharmaceutical campaigns, with expenditures for a given brand reaching well over several million dollars per month, could include: TV through gross rating points, printed matter, computer banners, radio exposure, etc. Characterizing the different factors through the approaches described above could result in a table with over 500 columns that define advertising expenditures and exposures. An analysis of 500 input columns evaluated 2 at a time results in over 124,750 comparisons. The situation may be even more difficult in that the data is collected daily and is highly autocorrelated. A complete and rigorous statistical analysis could take many days or weeks.